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URL: https://willitrunai.com/can-run/starcoder2-15b-on-arc-b570-10gb

⇱ StarCoder2 15B on Intel Arc B570 10GB? No — Alternatives

Can StarCoder2 15B run on Intel Arc B570 10GB?

YES — With NVFP4

D40Poor
○Estimated from fit model

StarCoder2 15B needs ~11.5 GB VRAM. Intel Arc B570 10GB has 10.0 GB. With NVFP4 quantization, expect ~16 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: LowStack: StandardBottleneck: Host offload
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Operating mode

Choose the run profile you care about

Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.

Current mode

Balanced

Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.

StarCoder2 15B at Q5_K_M needs 13.9 GB — too much for Intel Arc B570 10GB (10.0 GB). Runs at NVFP4 (11.5 GB) with medium quality. 3 quantization levels fit.
Capabilities:

Select quantization to explore

Q5_K_M (High quality) — 13.9 GB, exceeds 10.0 GB available
13.9 GB required10.0 GB available
139% VRAM needed

3.9 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

8.2 tok/s

TTFT

23498 ms

Safe context

4K

Memory

13.9 GB / 10.0 GB

Offload

30%

Memory breakdown

Weights10.8 GB
KV Cache1.2 GB
Runtime0.9 GB
Headroom1.0 GB

See how fast it feels

With memory offload — actual speed may be lower
See how fast it feelsStarCoder2 15B on Intel Arc B570 10GB
1st promptCold start — includes initialization
>Write a Python function to sort a list
Here's a Python function that sorts a list using quicksort: def quicksort(arr): if len(arr) <= 1: return arr pivot = arr[len(arr) // 2] left = [x for x in arr if x < pivot] middle = [x for x in arr if x == pivot] right = [x for x in arr if x > pivot] return quicksort(left) + middle + quicksort(right) # Example usage numbers = [3, 6, 8, 10, 1, 2, 1] print(quicksort(numbers)) # [1, 1, 2, 3, 6, 8, 10] The algorithm picks a pivot, partitions the list into smaller, equal, and larger elements, then recursively sorts each partition and concatenates the results.
2nd promptWarm — model ready, no init overhead
>Now add type hints and a docstring
def quicksort(arr: list[int]) -> list[int]: """Sort a list using the quicksort algorithm. Args: arr: List of integers to sort. Returns: A new sorted list. """ if len(arr) <= 1: return arr pivot = arr[len(arr) // 2] left = [x for x in arr if x < pivot] middle = [x for x in arr if x == pivot] right = [x for x in arr if x > pivot] return quicksort(left) + middle + quicksort(right) Added type hints for the input and return type, plus a Google-style docstring.
Estimated: 8.2 tok/s decode · 23.5s TTFT (warm) · 21 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Runtime ecosystem is narrower than CUDA

Intel GPUs can look attractive on memory per dollar, but local AI tooling, kernels, and model coverage are still broader and easier on CUDA today.

Best improvement path

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Prefer CUDA if you want the path of least resistance

If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatFToo heavy9.0 tok/s11697 ms4K
CodingFToo heavy8.2 tok/s23498 ms4K
Agentic CodingFToo heavy6.9 tok/s40573 ms4K
ReasoningFToo heavy8.2 tok/s27771 ms4K
RAGFToo heavy6.9 tok/s50716 ms4K

Quantization options

How StarCoder2 15B (15B params) fits at each quantization level on Intel Arc B570 10GB (10.0 GB usable).

QuantBitsVRAMQualityFit
Q2_KBest for your GPU
2
5.9 GB
LowC54
Q3_K_S
3
7.4 GB
LowF0
NVFP4
4
8.4 GB
MediumF0
Q4_K_M
4
9.2 GB
MediumF0
Q5_K_M
5
10.8 GB
HighF0
Q6_K
6
12.3 GB
HighF0
Q8_0
8
16.1 GB
Very HighF0
F16
16
30.7 GB
MaximumF0

Get started

Copy-paste commands to run StarCoder2 15B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \ --hf-repo "bigcode/starcoder2-15b" \ --hf-file "starcoder2-15b-Q5_K_M.gguf" \ -c 4096 -ngl 99

Upgrade options

Hardware that runs StarCoder2 15B well

👁 Intel
Intel Arc B580 12GBBest value
12 GB VRAM (+2)456 GB/s (+76)
D
Makes the model fit on the accelerator instead of staying completely out of reach.12.4 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Raises estimated decode speed by about 51%.

~$249 MSRP

👁 Intel
Intel Arc A770 16GBBudget pick
16 GB VRAM (+6)560 GB/s (+180)
C
Makes the model fit on the accelerator instead of staying completely out of reach.26 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$349 MSRP

👁 Intel
Intel Arc Pro B50 16GBIntel upgrade
16 GB VRAM (+6)
C
Makes the model fit on the accelerator instead of staying completely out of reach.12.5 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$399 MSRP

👁 NVIDIA
RTX 4090 24GBBiggest leap
24 GB VRAM (+14)1008 GB/s (+628)
B
Makes the model fit on the accelerator instead of staying completely out of reach.80.8 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$1,599 MSRP

Frequently asked questions

See all results for Intel Arc B570 10GBSee all hardware for StarCoder2 15B